Incrementality measurement using causal data science can also provide advertisers with the ability to measure external influencers. Whether if it was seasonality driven such as a major holiday, or a promotion offered to users. 

 

Causal data science basically comes to answer: “What would have happened if this change never happened?” allowing advertisers to come up with creative questions and learn about the actual causes behind marketing performance changes to continuously improve their own strategy.

 

INCRMNTAL is an incrementality measurement platform using causal data science. 

How granular is incrementality measurement ?

This question is asked pretty often. Advertisers who got used to device level attribution, have been very happy with the ability to get reports on the most granular level. For this reason, digital and mobile first advertisers have been hesitant in testing channels like TV, influencers, out of home, where attribution data was not available.

 

Cross platform advertisers knew of methodologies such as Media Mix Modeling, which allowed cross channel measurement, but given the amount of data needed, the granularity that MMM was able to provide was medium and channel at best.

 

Incrementality measurement using causal data science allows measurement down to the ad group level for digital marketing, and down to the campaign level for untrackable mediums such as TV. The methodology doesn’t require user level data, as the measurement focuses on the value of marketing activities rather than clicks or exposures generated.

 

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